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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Agenda\n",
      "\n",
      "- Define the problem and the approach\n",
      "- Data basics: loading data, looking at your data, basic commands\n",
      "- Handling missing values\n",
      "- <p style=\"color: red\">Intro to scikit-learn</p>\n",
      "- Grouping and aggregating data\n",
      "- Feature selection\n",
      "- Fitting and evaluating a model\n",
      "- Deploying your work"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##In this notebook you will\n",
      "\n",
      "- Take a tour of `scikit-learn` and learn what it's used for\n",
      "- Build a toy classifier\n",
      "- Make and vizualize a decision tree\n",
      "- Build your own regression model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##[scikit-learn](http://scikit-learn.org/stable/)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Consistent APIs\n",
      "Algorithms are implemented with the same core functions:\n",
      "\n",
      "- fit = train an algorithm\n",
      "- predict = predict the value for a given record\n",
      "- predict_proba = predict the probability of all possible classes for a given record (classification only)\n",
      "- transform = alter your data based on a given preprocessor (i.e. normalize or scale your data) (preprocessing/unsuperivsed)\n",
      "- fit_transform = train a preprocessor and then transform the data in a single step (preprocessing/unsuperivsed)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<blockquote class=\"twitter-tweet\"><p>Scikit-Learn reply to today&#39;s <a href=\"https://twitter.com/wiseio\">@wiseio</a> Random Forest benchmark: <a href=\"https://t.co/El5at9KvHS\">https://t.co/El5at9KvHS</a> \u2026 Coming soon in the next 0.14 stable release!</p>&mdash; Gilles Louppe (@glouppe) <a href=\"https://twitter.com/glouppe/statuses/357250042538622976\">July 16, 2013</a></blockquote>\n",
      "<script async src=\"//platform.twitter.com/widgets.js\" charset=\"utf-8\"></script>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_iris\n",
      "\n",
      "iris = load_iris()\n",
      "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
      "df['species'] = iris.target"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import SVC\n",
      "from sklearn.neighbors import KNeighborsClassifier"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svm_clf = SVC()\n",
      "neighbors_clf = KNeighborsClassifier()\n",
      "clfs = [\n",
      "    (\"svc\", SVC()),\n",
      "    (\"KNN\", KNeighborsClassifier())\n",
      "    ]\n",
      "for name, clf in clfs:\n",
      "    clf.fit(df[iris.feature_names], df.species)\n",
      "    print name, clf.predict(iris.data)\n",
      "    print \"*\"*80"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "svc [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
        " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
        " 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2]\n",
        "********************************************************************************\n",
        "KNN [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
        " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1\n",
        " 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 2 2 2 2\n",
        " 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
        " 2 2]\n",
        "********************************************************************************\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Try a [RandomForetClassifier](http://scikit-learn.org/stable/modules/ensemble.html#random-forests) (`from sklearn.ensemble import RandomForestClassifier`) and see how it does"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn ...\n",
      "clf = ..\n",
      "clf.fit(..., ...)\n",
      "clf.predict(...)\n",
      "pd.crosstab(..., ...)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Predicted</th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Actual</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 50</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  0</td>\n",
        "      <td> 50</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "Predicted   0   1   2\n",
        "Actual               \n",
        "0          50   0   0\n",
        "1           0  50   0\n",
        "2           0   0  50"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##A Quick Decision Tree Example\n",
      "####[more examples](http://scikit-learn.org/stable/auto_examples/index.html#decision-trees)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import the decision tree library and train a classifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn import tree\n",
      "\n",
      "clf = tree.DecisionTreeClassifier(max_features=\"auto\",\n",
      "                                  min_samples_leaf=10)\n",
      "clf.fit(df[iris.feature_names], df.species)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n",
        "            max_depth=None, max_features='auto', min_density=None,\n",
        "            min_samples_leaf=10, min_samples_split=2, random_state=None,\n",
        "            splitter='best')"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Write the tree to a file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.externals.six import StringIO\n",
      "with open(\"iris.dot\", 'w') as f:\n",
      "    f = tree.export_graphviz(clf, out_file=f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####We're going to convert it to a .png so we can display it in the notebook"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%sh\n",
      "# you will need to install graphviz \n",
      "#(http://www.graphviz.org/Download..php) and pydot (pip install pydot)\n",
      "dot -Tpng iris.dot -o iris.png"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import Image\n",
      "Image(\"iris.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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QpQFIs3v37sjIyFOnTqmrqzMdS0ehrq5+5syZyMjInTt3Mh0LAIA8Q5YG0KSU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Pjz5f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\nmKUBAAAAAKii/wf7JrZsl1GmtwAAAABJRU5ErkJggg==\n",
       "prompt_number": 12,
       "text": [
        "<IPython.core.display.Image at 0x1082d70d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##What to do first?\n",
      "[Andy Mueller](http://peekaboo-vision.blogspot.com/) (scikit-learn contributor) put together this [cheat sheet](http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png) a few months ago which is extremely helpful."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Image(url=\"http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png\",\n",
      "      width=700)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<img src=\"http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png\" width=\"700\"/>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.Image at 0x1082d7150>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Try building a linear regression model for the Boston Housing Dataset to predict Home Prices"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.datasets import load_boston\n",
      "boston = load_boston()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Prep the data\n",
      "\n",
      "- Create a dataframe with the Boston data\n",
      "- snake_case the columns\n",
      "- print to the console"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \n",
      "df = pd.DataFrame(..., columns=...)\n",
      "# add in prices\n",
      "df[...] = ...\n",
      "df.columns = ...\n",
      "print len(df)==506\n",
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "506\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>crim</th>\n",
        "      <th>zn</th>\n",
        "      <th>indus</th>\n",
        "      <th>chas</th>\n",
        "      <th>nox</th>\n",
        "      <th>rm</th>\n",
        "      <th>age</th>\n",
        "      <th>dis</th>\n",
        "      <th>rad</th>\n",
        "      <th>tax</th>\n",
        "      <th>ptratio</th>\n",
        "      <th>b</th>\n",
        "      <th>lstat</th>\n",
        "      <th>price</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.00632</td>\n",
        "      <td> 18</td>\n",
        "      <td> 2.31</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.538</td>\n",
        "      <td> 6.575</td>\n",
        "      <td> 65.2</td>\n",
        "      <td> 4.0900</td>\n",
        "      <td> 1</td>\n",
        "      <td> 296</td>\n",
        "      <td> 15.3</td>\n",
        "      <td> 396.90</td>\n",
        "      <td> 4.98</td>\n",
        "      <td> 24.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.02731</td>\n",
        "      <td>  0</td>\n",
        "      <td> 7.07</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.469</td>\n",
        "      <td> 6.421</td>\n",
        "      <td> 78.9</td>\n",
        "      <td> 4.9671</td>\n",
        "      <td> 2</td>\n",
        "      <td> 242</td>\n",
        "      <td> 17.8</td>\n",
        "      <td> 396.90</td>\n",
        "      <td> 9.14</td>\n",
        "      <td> 21.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.02729</td>\n",
        "      <td>  0</td>\n",
        "      <td> 7.07</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.469</td>\n",
        "      <td> 7.185</td>\n",
        "      <td> 61.1</td>\n",
        "      <td> 4.9671</td>\n",
        "      <td> 2</td>\n",
        "      <td> 242</td>\n",
        "      <td> 17.8</td>\n",
        "      <td> 392.83</td>\n",
        "      <td> 4.03</td>\n",
        "      <td> 34.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.03237</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2.18</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.458</td>\n",
        "      <td> 6.998</td>\n",
        "      <td> 45.8</td>\n",
        "      <td> 6.0622</td>\n",
        "      <td> 3</td>\n",
        "      <td> 222</td>\n",
        "      <td> 18.7</td>\n",
        "      <td> 394.63</td>\n",
        "      <td> 2.94</td>\n",
        "      <td> 33.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.06905</td>\n",
        "      <td>  0</td>\n",
        "      <td> 2.18</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.458</td>\n",
        "      <td> 7.147</td>\n",
        "      <td> 54.2</td>\n",
        "      <td> 6.0622</td>\n",
        "      <td> 3</td>\n",
        "      <td> 222</td>\n",
        "      <td> 18.7</td>\n",
        "      <td> 396.90</td>\n",
        "      <td> 5.33</td>\n",
        "      <td> 36.2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "      crim  zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio       b  lstat  \\\n",
        "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3  396.90   4.98   \n",
        "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8  396.90   9.14   \n",
        "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8  392.83   4.03   \n",
        "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7  394.63   2.94   \n",
        "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7  396.90   5.33   \n",
        "\n",
        "   price  \n",
        "0   24.0  \n",
        "1   21.6  \n",
        "2   34.7  \n",
        "3   33.4  \n",
        "4   36.2  "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Fit your model\n",
      "\n",
      "- define your features\n",
      "- create a LinearRegression\n",
      "- fit the model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import ...\n",
      "\n",
      "features = [...]\n",
      "lm = ..\n",
      "lm..."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Plot the predicted values against the actual values"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# add your actual vs. predicted points\n",
      "pl.scatter(df.price, lm.predict(df[features]))\n",
      "# add the line of perfect fit\n",
      "straight_line = np.arange(0, 60)\n",
      "pl.plot(straight_line, straight_line)\n",
      "pl.title(\"Fitted Values\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.text.Text at 0x1084bf9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SohEfrwa2GzRQsFovVOXFI1i+99IievACwRXMxar8GBOjsWhRITNmGImKkrn/fi8hIeVy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\nvbvw4QWCxkZ48H6I3eVl5b489v5YzPThsQyKKV9DUDEbP3j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       "text": [
        "<matplotlib.figure.Figure at 0x1084e2290>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Redo the model, but this time have a training/test set."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##We just did the following\n",
      "\n",
      "- Learned about `scikit-learn` and what it's used for\n",
      "- Used CART to build a decision tree classifier and visualized it\n",
      "- Build a regression model using `scikit-learn`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}